1. Field of the Invention
The present invention generally concerns magnetic resonance tomography (MRT) as used in medicine for the examination of patients. The present invention concerns a method as well as an MRT system for the implementation of the method that enable the acquisition of artifact-free or low-artifact slice images without SNR (signal-to-noise ratio) loss.
2. Description of the Prior Art
MRT is based on the physical phenomenon of nuclear magnetic resonance and has been successfully used as an imaging method for over 15 years in medicine and biophysics. In this examination modality, the subject is exposed to a strong, constant magnetic field. The nuclear spins of the atoms in the subject, which were previously randomly oriented, thereby align.
Radio-frequency energy can now excite these “ordered” nuclear spins to a specific oscillation (resonance frequency). In MRT, this oscillation generates the actual measurement signal (RF response signal) which is acquired by means of appropriate reception coils. By the use of inhomogeneous magnetic fields generated by gradient coils, the measurement subject can be spatially coded in all three spatial directions. The slice to be imaged can be freely selected, and slice images of the human body can be acquired in all directions. MRT as a slice imaging modality in medical diagnostics is primarily considered as a “non-invasive” examination modality with a manifold contrast capability. Due to the excellent representation capability of soft tissue, the MRT has developed into a modality that is superior to x-ray computed tomography (CT). MRT today is based on the application of spin echo and gradient echo sequences that enable an excellent image quality with measurement times in the range of minutes.
The constant technical development of the components of MRT apparatuses and the introduction of faster imaging sequences continually open more fields of use in medicine for MRT. Real-time imaging for the support of minimally invasive surgery, functional imaging in neurology and perfusion measurement in cardiology are only a few examples. In spite of the technical advances in the construction of MRT apparatuses, acquisition times and the signal-to-noise ratio (SNR) of an MRT image remain limiting factors for many applications of MRT in medical diagnostics.
Particularly in functional imaging, in which a significant movement of the subject or parts of the subject is present (blood flow, heart movement, peristalsis of the abdomen, etc.), a reduction of the data acquisition time with a constant SNR is desirable. Movement generally causes artifacts (such as, for example, movement artifacts) in an MRT image that increase with the duration of the data acquisition time. In order to improve the image quality, it might be considered to acquire a number of images and to later superimpose them, but this does not always lead to an improvement of the overall image quality, particularly with regard to movement artifacts. For example, the SNR is improved while the movement artifacts increase.
With consistent SNR, one approach to shorten the measurement time is to reduce the quantity of the image data to be acquired. In order to obtain a complete image from such a reduced data set, either the missing data must be reconstructed with suitable algorithms or the missing image portion must be corrected from the reduced data. The acquisition of the data in MRT occurs (according to FIG. 2) in a mathematical space known as k-space (frequency domain). The MRT image in the image domain is linked with the MRT data 23 in k-space by means of Fourier transformation 24. The spatial coding of the subject which spans k-space occurs by means of gradients in all three spatial directions. In the case of 2D imaging, a distinction is made between the slice selection direction (establishes an acquisition slice in the subject, typically the z-axis) and the phase coding direction (determines the second dimensional within the slice, typically the y-axis). In the case of 3D imaging, the slice selection direction is replaced by a second phase coding direction. Without limitation of the generality, in the discussion that follows a two-dimensional Cartesian k-space is assumed that is sampled line-by-line. The data of a single k-space line are frequency-coded upon readout by means of a gradient. Each line in k-space has a separation Δky that is generated by a phase coding step. Since the phase coding occupies a lot of time in comparison to the other spatial codings, to reduce the image measurement time most techniques (for example “partial parallel acquisition” PPA) are based on a reduction of the number of time-consuming phase coding steps. The fundamental idea in PPA imaging is that the k-space data are not acquired by a single coil but rather (according to FIG. 3) by a (for example) linear arrangement of component coils (coil 1 through coil 4), a coil array. Each of the spatially independent coils of the array carries certain spatial information, which is used in order to achieve a complete spatial coding by a combination of the simultaneously acquired coil data 26.1, 26.2, 26.3, 26.4. This means that a number of other shifted (shown white in the following figures) lines not scanned in k-space can be determined from a single acquired k-space line (shown grey in the following figures).
The PPA methods thus use spatial information contained in the components of a coil arrangement in order to partially replace the time-consuming phase coding that is normally generated using a phase gradient. The image measurement time is thereby reduced corresponding to the ratio of the number of the lines of the reduced data set to the number of the lines of the conventional (thus complete) data set. In a typical acquisition of data by PPA, in comparison to the conventional acquisition only a fraction (½, ⅓, ¼, etc.) of the phase coding lines is acquired. A special reconstruction is then applied to the data in order to reconstruct the lacking k-space lines and therewith to obtain the whole field of view (FOV)-image in a fraction of the time.
The image reconstruction method that is used, which normally is an algebraic method, corresponds to the respective PPA technique that is used. The most widely known PPA techniques are SMASH (Simultaneously Acquisition of Spatial Harmonics), SENSE (Sensitivity Encoding) and GRAPPA (Generalized Autocalibration PPA) with their respective derivates (G-SMASH, AUTO-SMASH, VD-AUTO-SMASH etc.).
In all PPA techniques the algebraic reconstruction of the missing k-space lines additionally requires the determination of the coil sensitivity of each component coil (participating in the measurement), which is symbolized by the arrow 28 in FIG. 3. A complete reconstruction of all k-space lines is possible only given such knowledge of the coil sensitivities, and the image 25 is obtained by subsequent Fourier transformation (arrow 27).
In the conventional PPA techniques, the determination of the coil sensitivities ensues by calibration scans, either at the beginning of the diagnostic data acquisition in the form of pre-scans, or during the diagnostic data acquisition the form of integrated scans 29 (ACS lines, autocalibration signals), which are shown in FIG. 4 as black k-space lines in the middle region of the k-matrix (k-space slice).
The coil sensitivities are in fact harmonic functions that can be well-approximated via only a few calibration scan lines, preferably from the middle region of the k-matrix, which predominantly contains contrast information. Nevertheless, the measurement of calibration scan lines significantly lengthens the total acquisition time and increases the degree of movement artifacts in the reconstructed image 25.
The prior art offers a possibility to suppress or, respectively, to minimize movement artifacts with consistent SNR in spite of time-consuming measurements of calibration scan lines.
In one method for this purpose is explained using FIG. 5: a number of low-resolution PPA series 26 are acquired in temporal series. In FIG. 5 two series 26 are shown, wherein the calibration scan lines 29 necessary for PPA reconstruction have been concomitantly measured and are shown black. Due to the low resolution, each series inherently exhibits a relatively low SNR, however due to the short acquisition time movement artifacts of each series are also significantly reduced. Images with far fewer artifacts thus can be generated by subsequent superimposition of the images from both series of PPA-reconstructed images, with the original SNR being regained by the superimposition.
A disadvantage in this method is the fact that, as before, the data in the calibration scan lines must be additionally acquired for each slice, or for each series, in order to be able to determine the coil sensitivities necessary for the PPA reconstruction. This is true both for pre-scans and for integrated scans.